When an incident ticket is raised classification of the incident tickets may be automated. A user may raise his/her issue in natural language as incident ticket. Currently when the user raises the incident ticket he/she may be presented a few choices representing a class in a historical ticket data. The incident ticket may be classified using the historical ticket data. Typically, the choices which may be displayed to the user do not classify the incident ticket correctly. The historical ticket data may be erroneous. Hence accuracy of classification of the incident ticket may be poor.
The choices may be retrieved from the historical ticket data by a predictor component. The predictor component may be usually based on positive models. The positive models may return a class if the incident ticket belongs to the class. But as the historical ticket database may at least 30% of incorrect incident tickets the user may expect the classes to be wrongly returned for a significant number of times. Due to this user may be forced to raise a ticket under a wrong class. Hence a delay may be caused in resolution of issue. Hence there is a need to improve the classification for different classes.